Intuitionistic time series fuzzy inference system. (June 2019)

• Record Type: Journal Article
• Authors: Egrioglu, Erol; Bas, Eren; Yolcu, Ozge Cagcag; Yolcu, Ufuk
• Is Part Of: Engineering applications of artificial intelligence. Volume 82(2019)
• Page Start: 175
• Page End: 183
• Publication Date: 2019-06
• Subjects: Fuzzy inference system -- Intuitionistic fuzzy c-means -- Sigma-pi neural network -- Time series prediction -- Particle swarm optimization; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2019.03.024
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Although adaptive network fuzzy inference system and fuzzy functions approach can be utilized as a prediction tool, they have been not designed for prediction problem and they ignore the dependency structure of time series observations. From this viewpoint, making a design of the method that considers the dependency structure of observations will provide to get more accurate prediction. In this study, an intuitionistic time series fuzzy inference system (I-TSFIS) has been proposed. In the I-TSFIS, in just the same way as in the intuitionistic fuzzy inference systems, not only the membership values and crisp observations but also the non-membership values are used as inputs. Moreover, due to the use of crisp values as targets and outputs, the output does not need to be deffuzzified. Non-linear relationships between inputs and outputs of the proposed I-TSFIS are determined by Sigma-Pi neural network (SP-NN). The obtaining of optimal weights of SP-NN is performed by modified particle swarm optimization. And also I-TSFIS uses intuitionistic fuzzy C-means to obtain fuzzy clusters, membership and non-membership values of observations for these clusters. To evaluate the prediction performance of the proposed I-TSFIS, various real-life time series data sets have been analyzed and the results demonstrate the superior prediction ability of the proposed I-TSFIS. Highlights: An IS with dependency structure of observations can ensure successful prediction. We have proposed an intuitionistic time series fuzzy inference system (I-TSFIS). I-TSFIS, as inputs, uses the membership, the non-membership and the crisp values. I-TSFIS forms non-linear relationships between inputs and outputs via SP-NN. The training of SP-NN is performed by modified particle swarm optimization.